Many oil industry applications require the analysis of downhole fluids. In the prior art this was typically done by bringing samples to the surface using sealed containers, and sending the samples for laboratory measurements. A number of practical limitations are associated with this approach. The main concern usually is that the samples taken to the surface may not be representative of the downhole geologic formation. This is due to the fact that only limited sample material from a limited number of downhole locations can be extracted and taken to the surface, and thus laboratory testing provides only an incomplete picture of the downhole conditions. Furthermore, samples are often contaminated with mud filtrate, and therefore are not truly representative of the native formation fluids.
More recently, fluid analysis became possible using pumpout formation testers that provide downhole measurements of certain fluid properties and enable the collection of a large number of representative samples stored at downhole conditions. Three families of such tools have been introduced in the past—the modular dynamic formation tester (MDT) by Schlumberger, the Reservoir Characterization Instrument (RCI) by Baker Atlas, and most recently the Reservoir Description Tool (RDT) by Halliburton. These tools are generally designed to obtain representative formation fluid samples and provide key petrophysical information to determine the reservoir volume, producibility of a formation, type and composition of the movable fluids, and to predict reservoir behavior during production.
One of the remaining problems encountered in the operation of such tools is to avoid contamination of the fluid naturally present in the formation with other fluids, in particular the various types of drilling muds used in drilling operations. Drilling mud, also known as drilling fluid, is typically pumped down the center of the hollow drill stem to emerge again at the surface of the borehole. It lubricates the drill shaft, cools the borehole, carries away the drilling detritus, and serves as a wetting-phase, which facilitates the flow of hydrocarbons from the formation and into the borehole. Various types of drilling muds are generally classified based on the type of filtrate used therein. The mud filtrate chosen dictates the mud's function and performance, as well as formation invasion effects.
There are two major types of mud filtrates: water-based and oil-based. Water-based mud (WBM) filtrates include, but are not limited to, freshwater, seawater, saltwater (brine) and others, or a combination of any of these fluids. In the oil-based mud (OBM), the filtrate is an oil product, such as diesel or mineral oil. More generally, oil-based mud is characterized as any type of non-aqueous fluid. For the purposes of the present disclosure, oil-based mud also includes the recently developed variety of oil mud that is also referred to as synthetic-based muds. These synthetic-based muds include, without limitation, olefinic-, naphthenic-, and paraffinic-based compounds. Dependent on the type of mud used in the drilling process, different factors affect the ability of the tool the accurately estimate the contamination levels at a given point during pumpout.
For WBM drilling, mixing with the formation fluid is considered an immiscible process, and determination of the degree of contamination of the fluid using is relatively straightforward. More challenging is the problem of estimating the degree of contamination in OBM drilling when attempting to obtain high quality formation fluid samples, because these mud filtrate fluids are mixed in the formation oil. This mixing can be immiscible or miscible, but either way complicate the determination of the degree of contamination (with immiscible invasion the fluids to not dissolve with each other but mix, with miscible mixing the fluids dissolve in a diffusion process). For example, the presence of even small volumes of oil-base filtrate in the sample can significantly alter the properties of the formation oil. As a result, in-situ quantification of the oil-base material contamination in the formation oil is difficult, and poorly quantified samples may not be representative of the formation fluid of interest.
Additional difficulties are presented in differentiating oil-based mud from connate oil when oil-based filtrate invades the formation. One method for differentiating oil-based mud from connate oil is disclosed in U.S. Pat. No. 6,107,796, owned by the assignee of the present invention, which is incorporated herein by reference. However, no reliable method has been provided for determining the level of contamination of mud filtrate in formation fluids.
The most frequently used prior art approach to estimating contamination has been based on the optical properties of the fluids entering a tool. Schlumberger provides for in-situ contamination estimation using an Optical Fluid Analyzer (OFA). Baker-Atlas also offers a service similar to the OFA. The OFA exploits the differences in the absorption spectrum (i.e., color contrast) between the OBM contaminant and the formation fluid to deconvolute the spectrum from a fluid measurement (see, e.g., U.S. Pat. Nos. 6,178,815, 6,274,865, 6,343,507 and 6,350,986, which are incorporated herein by reference for background). The OFA measures the optical density (OD) of the flowing fluid and uses empirical relationships to transform the OD into data on contamination by determining the composition of the measured absorbed light spectrum from the sample. Based on this absorption spectrum one can estimate the types of materials present in the fluid and the proportion of each material in the fluid. While the industry has learned how to interpret OFA data over the years, it still is not robust in certain applications where the color contrast is small, or is masked, as is frequently the case in light oils and condensates. One problem with this approach is that it assumes that the measured property is directly linked to the contamination, which may not necessarily be the case.
Another approach to contamination estimation is to use electrical resistivity methods, which involve the measurement of the apparent resistivity of fluids entering the tool. While these measurements are straightforward to implement and, for example, can easily distinguish between oil and water it cannot reliably distinguish contaminants in OBM situations. Various other sensors measuring optical properties, resistivity, capacitance and others within formation sampling tools have been used to estimate levels of fluid contamination during the pump-out phase, but no robust solution has been found yet.
A more recent approach to contamination estimation is provided by the use of nuclear magnetic resonance (NMR) measurements. NMR measurements of geologic formations may be done using, for example, the MRIL® tool made by NUMAR, a Halliburton company, and the CMR family of tools made by Schlumberger. Details of the structure of the MRIL® tool and the measurement techniques it uses are discussed in U.S. Pat. Nos. 4,710,713; 4,717,876; 4,717,877; 4,717,878; 4,939,648; 5,055,787; 5,055,78; 5,212,447; 5,280,243; 5,309,098; 5,412,320; 5,517,115, 5,557,200; 5,696,448; 5,936,405; 6,005,389; 6,023,164; 6,051,973; 6,107,796; 6,111,408; 6,242,913; 6,255,819; 6,268,726; 6,362,619; 6,512,371; 6,525,534; 6,531,868; 6,541,969; 6,577,125 and 6,583,621, all of which are commonly owned by the assignee of the present application. The CMR tool is described, for example, in U.S. Pat. Nos. 5,055,787 and 5,055,788 to Kleinberg et al. and further in “Novel NMR Apparatus for Investigating an External Sample,” by Kleinberg, Sezginer and Griffin, J. Magn. Reson. 97, 466-485, 1992. NMR devices, methods and pulse sequences for use in logging tools are also in U.S. Pat. Nos. 4,350,955 and 5,557,201. The content of the above patents and publications is hereby expressly incorporated by reference for background. A brief discussion of the main NMR measurement parameters follows.
Basic NMR Properties and Measurement Parameters
NMR measurements are based on exposing an assembly of magnetic moments, such as those of hydrogen nuclei, to a static magnetic field. The assembly tends to align along the direction of the magnetic field, resulting in a bulk magnetization. A magnetic field having direction perpendicular to the static magnetic field is applied to rotate the magnetic moments away from the direction of the bulk magnetization. The rate at which the rotated moments return to the equilibrium bulk magnetization after application of the oscillating magnetic field is characterized by the parameter T1, known as the spin-lattice relaxation time. T1 values are in the range of milliseconds to several seconds.
Another related and frequently used NMR parameter is the spin-spin relaxation time constant T2 (also known as transverse relaxation time), which is an expression of the relaxation due to inhomogeneities in the local magnetic field over the sensing volume of the fluid in the analyzer, e.g., a logging tool. In bulk fluids T2 basically equals T1, but may differ in heavy oil components, such as asphaltenes, resins, etc. Both relaxation times provide information about the properties of the formation fluid, such as the formation porosity and the composition and quantity of the formation fluid.
Another measurement parameter used in NMR is the formation diffusivity. Generally, diffusion refers to the motion of atoms in a gaseous or liquid state due to their thermal energy. The self-diffusion coefficient (D) of a fluid is inversely proportional to the viscosity (η) of the fluid, a parameter of considerable importance in borehole surveys. Stokes' equation yields that:D∝kT/η, (k=1.38×10−23 J/K)  (1)
Viscosity and diffusivity are both related to the translational motion of molecules and therefore are interrelated. At higher temperatures T, a molecule contains more energy and can move faster against a given “friction” η, therefore D is proportional to the temperature. Diffusivity is a property that can be precisely determined by NMR techniques without disturbing or altering the fluid. The relationship D∝T/η has been verified over a wide range of viscosities at different temperatures and pressures by NMR spin-echo experiments.
Relationships involving the NMR relaxation times T1 and T2 must be examined with care. The applicability of expressions of the form:T1, T2∝kT/η  (2)
is more limited than that of Eq. (1). The main reason is that gas/liquid mixtures have more than one relaxation mechanism: dipole-dipole for the liquid phase and mainly spin-rotation for the gas phase.
In a uniform magnetic field, diffusion has little effect on the decay rate of the measured NMR echoes. In a gradient magnetic field, however, diffusion causes atoms to move from their original positions to new ones, which also causes these atoms to acquire different phase shifts compared to atoms that did not move. This contributes to a faster rate of relaxation.
Recently, Halliburton introduced MRILab®, a logging tool with the ability to analyze key reservoir fluid properties, including fluid type, viscosity and gas-to-oil ratio (GOR), in real-time at reservoir temperature and pressure. MRILab® is based on NMR measurements and operates as a component of Halliburton's Reservoir Description Tool™ (RDT), making laboratory-quality measurements on reservoir fluids that are necessary for reservoir engineering and completion design. FIG. 5 shows a simplified diagram of a downhole NMR fluid analysis apparatus, such as the MRILab®, that provides NMR measurements to which the contamination estimation methods of the present disclosure can be applied in an illustrative embodiment. Fluids enter the device at the top and pass through two sections, referred to as polarization and resonance sections, respectively. Measurements are performed as the fluid flow passes through the device. U.S. application Ser. No. 10/109,072, which is hereby incorporated by reference, discloses details of this device, which are summarized for reference in Appendix A.
Turning back to the problem of contamination estimation, the fundamental difficulty in NMR-based approaches to such estimation is the lack of models that can predict the relaxation spectrum (i.e., the T1 or T2 spectrum) of a mixture of two fluids that are miscible. Existing NMR methods for estimating contamination, including when the MRILab® was first introduced, were based initially on a parameter called “sharpness.” The sharpness of an NMR distribution is defined as:
                              S          =                                    N              ⁢                                                ∑                  i                                                                                        ⁢                                                                            a                      i                      2                                        ⁡                                          (                                                                        ∑                                                      i                            =                            1                                                    N                                                ⁢                                                  a                          i                                                                    )                                                        2                                                                                    (                                  N                  -                  1                                )                            ⁢                                                (                                                            ∑                      i                                                                                                            ⁢                                          a                      i                                                        )                                2                                                    ,                                  ⁢                              for            ⁢                                                  ⁢            1                    ≤          i          ≤          N                ,                            (        3        )            
where ai are the amplitudes, and N is the number of components in a T1 distribution. (See Bouton, J. et al. “Assessment of Sample Contamination by Downhole NMR Fluid Analysis”, SPE-71714, presented at SPE ATCE, New Orleans, La. (2001) incorporated herein for background). Although it was thought that sharpness was sensitive to contamination levels down to 10%, experience has shown that sharpness in general is not a very robust indicator of contamination.
The driving idea behind the use of the sharpness parameter was that, while OBM has a narrow distribution (implying a lower S value), distributions associated with native crudes are broader. However, relaxation spectra of low viscosity crudes are also very narrow and in the low viscosity/high GOR case, it may not be possible to distinguish one species from the other. Furthermore, both bulk water and natural gas have narrow distributions. The information from sharpness is relative, in that it is an indicator of the changes taking place, but it may not be sufficient to define end point states quantitatively. Additionally, the sharpness parameter is derived from the T1 distribution, which to a certain extent can be affected by the level signal-to-noise ratio (SNR) or distribution shape, which may lead to changes in the T1 spectra that are unrelated to changes in contamination.
Given the difficulties using the prior art approaches, there exists a need for more accurate and robust methods for determining the level of contamination of mud filtrate in formation fluids.